The COVID-19 pandemic has struck the world and forced countries to go into lockdown including education sector. Students have been staying in hostels or houses, unable to go to university campuses. This situation has left university administrators no choice, but to have an online learning channel. Malaysian universities in particular have gone through many challenges to bring their online learning system up and ready to resume education process. However, students have found themselves caught in this situation (pure online learning) with no plan or readiness. Literature reviews showed that students encountered some challenges that could not be easily resolved. This study explored the challenges encountered by students of a government-linked university. This university is one of the largest in Malaysia with over 10 campuses across the country. This study collected 284 valid answers. The findings show that respondents lacked full readiness in this situation physically, environmentally, and psychologically with some differences in perspectives according to their gender, age, and residing state. Respondents were concerned about the implications of lockdown on their performance. The findings of this study indicate that a sudden switch to a pure online alternative creates considerable challenges to students who have no plans to be physically apart from classes. The findings also indicate that the current blended learning process which uses online learning as a support mechanism for face-to-face learning has faced a considerable challenge to replace it, particularly with unprepared students.

Background and Objectives: Brain tumors are complex diseases that require careful diagnosis and treatment. A minor error in the diagnosis may easily lead to significant consequences. Thus, one must place a premium on accurately identifying brain tumors. However, deep learning (DL) models often face challenges in obtaining sufficient medical imaging data due to legal, privacy, and technical barriers hindering data sharing between institutions. This study aims to implement a federated learning (FL) approach with privacy-preserving techniques (PPTs) directed toward segmenting brain tumor lesions in a distributed and privacy-aware manner.Methods: The suggested approach employs a model of 3D U-Net, which is trained using federated learning on the BraTS 2020 dataset. PPTs, such as differential privacy, are included to ensure data confidentiality while managing privacy and heterogeneity challenges with minimal communication overhead. The efficiency of the model is measured in terms of Dice similarity coefficients (DSCs) and 95% Hausdorff distances (HD95) concerning the target areas concerned by tumors, which include the whole tumor (WT), tumor core (TC), and enhancing tumor core (ET). Results: In the validation phase, the partial federated model achieved DSCs of 86.1%, 83.3%, and 79.8%, corresponding to 95% values of 25.3 mm, 8.61 mm, and 9.16 mm for WT, TC, and ET, respectively. On the final test set, the model demonstrated improved performance, achieving DSCs of 89.85%, 87.55%, and 86.6%, with HD95 values of 22.95 mm, 8.68 mm, and 8.32 mm for WT, TC, and ET, respectively, which indicates the effectiveness of the segmentation approach, and its privacy preservation.Conclusion: This study presents a highly competitive, collaborative federated learning model with PPTs that can successfully segment brain tumor lesions without compromising patient data confidentiality. Future work will improve model generalizability and extend the framework to other medical imaging tasks.

In this era of technology, cloud computing technology has become essential part of the IT services used the daily life. In this regard, website hosting services are gradually moving to the cloud. This adds new valued feature to the cloud-based websites and at the same time introduces new threats for such services. DDoS attack is one such serious threat. Covariance matrix approach is used in this article to detect such attacks. The results were encouraging, according to confusion matrix and ROC descriptors.

Recently, COVID-19 has infected a lot of people around the world. The healthcare systems are overwhelmed because of this virus. The intensive care unit (ICU) as a part of the healthcare sector has faced several challenges due to the poor information quality provided by current ICUs’ medical equipment management. IoT has raised the ability for vital data transfer in the healthcare sector of the new century. However, most of the existing paradigms have adopted IoT technology to track patients’ health statuses. Therefore, there is a lack of understanding on how to utilize such technology for ICUs’ medical equipment management. This paper proposes a novel IoT-based paradigm called IoT Based Paradigm for Medical Equipment Management Systems (IoT MEMS) to manage medical equipment of ICUs efficiently. It employs IoT technology to enhance the information flow between medical equipment management systems (THIS) and ICUs during the COVID-19 outbreak to ensure the highest level of transparency and fairness in reallocating medical equipment. We described in detail the theoretical and practical aspects of IoT MEMS. Adopting IoT MEMS will enhance hospital capacity and capability in mitigating COVID-19 efficiently. It will also positively influence the information quality of (THIS) and strengthen trust and transparency among the stakeholders.

Circular Economy (CE) offers a comprehensive approach to address the growing environmental, economic and social challenges posed by traditional linear economic systems, especially in achieving the UN Sustainable Development Goals (SDGs). about the ho. This study aims to examine the role of the circular economy in advancing specific SDGs, including SDG 12 (Responsible Management and Action), SDG 13 (Climate Action), SDG 6 (Clean Water and Sanitation), there is SDG 7 (Affordable and Clean) Energy) , SDG 8 (Decent Infrastructure and Economic Growth), and SDG 9 (Infrastructure, Innovation and Infrastructure Effective). By applying CE principles to these goals, the study identifies key areas where circular practices can contribute to global sustainability efforts. Findings suggest that, despite substantial benefits, several barriers—such as policy and regulatory barriers, financial and market challenges, and technological infrastructure gaps—impede the realization of circular economic potential completely The study concludes that structural changes, innovations to address these challenges , and emphasize the importance of intersectoral collaboration to facilitate the transition to a more sustainable and circular economy plant.

The internet is gaining a lot of importance day by day, especially with the emergence of cloud technology. This new technology has made a new computing service to end users that include, PaaS, SaaS. On the other hand, this technology was accompanied with some shortages. The most serious obstacle is the security challenges because of the cloud is characterized by computing resource sharing and multi-tenancy features and as a result flooding based denial of service attack has been observed. This effect on performance and quality of service on cloud. To overcome this security challenge, there are several methods to detect and prevent this kind of attack. Most of these approaches are using statistical and/or artificial intelligence methods. In this research paper a new model to detect flooding based DoS attack in cloud environment has been suggested consisting three phases. (1) The first-phase is to model the normal traffic pattern for baseline profiling and (2) the second phase is the intrusion detection processes and (3) finally prevention phase. The covariance Matrix mathematical model is used as detecting method. The phase (1) and (2) have been implemented in real test bed. From the result, it is proven that we can detect the flooding attack effectively.